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Dynamic Repertoire of Brain Networks in Mindfulness-Based Cognitive Therapy During Rumination: A Randomized Controlled Trial.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods and Materials › Leading Eigenvector Dynamics Analysis ↔ utilities/analyses_scripts/LEiDA_data.m, the whole file · a weak match · score 0.86 · phase coherence matrix, Hilbert transform, brain areas, leading eigenvector, fMRI, filtered
  2. [2] § Methods and Materials › Leading Eigenvector Dynamics Analysis ↔ utilities/analyses_scripts/EigenVectors_VoxelSpace.m, the whole file · a weak match · score 0.82 · phase coherence matrix, Hilbert transform, leading eigenvector, fMRI, space, LEiDA
  3. [3] § Methods and Materials › Study Design and Participants ↔ utilities/analyses_scripts/LEiDA_data.m, the whole file · a weak match · score 0.73 · phase coherence matrix, Hilbert transformed, Leading Eigenvector, fMRI, signal, parcellated
  4. [4] § Methods and Materials › Study Design and Participants ↔ utilities/analyses_scripts/EigenVectors_VoxelSpace.m, the whole file · a weak match · score 0.72 · phase coherence matrix, Hilbert transformed, Leading Eigenvector, fMRI, LEiDA, signal

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 124 lines · 4.6 KB · MIT · 2 matches

  1. function [V1_all,Time_sessions,Data_info] = LEiDA_data(data_dir,save_dir,n_areas,tmax,filter,flp,fhi,tr)
  2. %
  3. % For each subject compute the leading eigenvetor of the phase coherence
  4. % matrix calculated at each recording frame.
  5. %
  6. % INPUT:
  7. % data_dir directory where the parcellated fMRI data are saved;
  8. % fMRI data should be one file per subject; data can be a
  9. % matrix containing the time series in the formats .mat,
  10. % .1D and .txt; if data is as a struct then fMRI signal
  11. % should correspond to a field called data
  12. % save_dir directory where the leading eigenvectors will be saved
  13. % n_areas number of brain areas to consider for analysis
  14. % tmax maximum number of volumes across fMRI sessions
  15. % filter 0, temporal filtering (default); 1, no temporal filtering
  16. % flp lowpass frequency of filter
  17. % fhi highpass frequency of filter
  18. % tr TR of fMRI data
  19. %
  20. % OUTPUT:
  21. % V1_all (n_scans*(tmax-2) x n_areas) leading eigenvectors of all
  22. % subjects at each time point
  23. % Time_sessions (1 x n_scans*(tmax-2)) scan number of each leading
  24. % eigenvector
  25. % Data_info parcellated data
  26. %
  27. % Author: Joana Cabral, University of Minho, [email hidden]
  28. % Miguel Farinha, University of Minho, [email hidden]
  30. disp('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% LEADING EIGENVECTORS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%');
  31. disp(' ')
  33. % Get the files containg the data (.mat or .1D or .txt)
  34. Data_info = [dir([data_dir '*.mat']); dir([data_dir '*.1D']); dir([data_dir '*.txt'])];
  35. % Total number of scans that will be read
  36. n_scans = size(Data_info,1);
  37. disp(['Total number of scans in folder: ' num2str(n_scans)])
  38. disp(' ')
  40. % Matrix to store the leading eigenvectors of all subjects at each TR
  41. V1_all = zeros(n_scans*(tmax-2),n_areas);
  42. % Row vector with the scan number of each leading eigenvector
  43. Time_sessions = zeros(1,n_scans*(tmax-2));
  45. t_all = 0;
  46. discarded = [];
  47. idx_data = [];
  48. for s = 1:n_scans
  50. disp(['Computing the leading eigenvectors for scan ' num2str(s) ' ' Data_info(s).name]);
  52. % Handling the data differently depending on the file extension:
  53. % Regardless of the extension we import the data
  54. signal = importdata([data_dir Data_info(s).name]);
  55. if isstruct(signal)
  56. % Load the fMRI signal from the struct signal variable
  57. % fMRI data should be stored in a field called data
  58. signal = signal.data;
  59. end
  60. % Selecting only the areas specified by the user
  61. signal = signal(1:n_areas,:);
  63. if any(isnan(signal(:))) || any(isinf(signal(:))) || any(all(signal == 0,2))
  64. disp(' - NaN, Inf or rows of 0s were found -> Discarded');
  65. discarded = cat(2,discarded,s);
  66. else
  67. % Add index of participant to idx_data
  68. idx_data = cat(2,idx_data,s);
  70. % De-meaning the fMRI signal
  71. for n = 1:n_areas
  72. signal(n,:) = detrend(signal(n,:) - mean(signal(n,:)));
  73. end
  75. % Apply temporal filtering to the parcellated data
  76. if filter
  77. signal = TemporalFiltering(signal,flp,fhi,tr);
  78. end
  80. % Get the fMRI signal phase using the Hilbert transform
  81. for seed = 1:n_areas
  82. signal(seed,:) = angle(hilbert(signal(seed,:)));
  83. end
  85. % Compute leading eigenvector of each phase coherence matrix
  86. for t = 2:size(signal,2)-1 % exclude 1st and last TR of each fMRI signal
  88. % Save the leading eigenvector for time t
  89. [v1,~] = eigs(cos(signal(:,t)-signal(:,t)'),1);
  91. if sum(v1) > 0 % for eigenvectors with sum of entries > 0
  92. v1 = -v1;
  93. end
  95. t_all = t_all + 1; % time point in V1_all
  97. % row t_all correponds to the computed eigenvector at time t for subject s
  98. V1_all(t_all,:) = v1;
  100. % to which subject the computed leading eigenvector belongs
  101. Time_sessions(t_all) = s;
  102. end
  103. end
  104. end
  105. % Reduce size in case some scans have less TRs than tmax
  106. % In this case, these lines of code will not result in changes
  107. V1_all(t_all+1:end,:) = [];
  108. Time_sessions(:,t_all+1:end) = [];
  110. disp(' ')
  111. disp(['Total number of scans used to compute the leading eigenvectors: ' num2str(length(idx_data))]);
  113. for d = 1:length(discarded)
  114. disp(['Attention: ' Data_info(discarded(d)).name ' was discarded'])
  115. end
  116. disp(' ')
  118. % Name of the file to save output
  119. save_file = 'LEiDA_EigenVectors.mat';
  121. save([save_dir save_file], 'V1_all','Time_sessions','Data_info','idx_data')
  123. disp(['fMRI Phase Leading Eigenvectors saved successfully as ' save_file])
  124. disp(' ')

LEiDA_data.m at commit ab03cbb, under MIT · at the source

Overview

Authors: Anne Maj van der Velden1,2,3,4,5, Jakub Vohryzek5,6,7, Paulina Clara Dagnino6, Willem Kuyken2, Jesus Montero-Marin2,8,9,10, Guusje Collin3,4,11, Morten L. Kringelbach1,2,5,7, Henricus G. Ruhe3,4
  1. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
  2. Department of Psychiatry, University of Oxford, Oxford, United Kingdom
  3. Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
  4. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
  5. Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
  6. Centre for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
  7. International Centre for Flourishing, Universities of Oxford (UK), Aarhus (DK), and Pompeu Fabra (Spain)
  8. Teaching, Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Barcelona, Spain
  9. Center for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
  10. Contemplative Studies Centre, School of Psychological Sciences, University of Melbourne, Melbourne, Australia
  11. McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
Institutions: Radboud University Nijmegen (Netherlands); Aarhus University (Denmark); Radboud University Medical Center (Netherlands); University of Oxford (United Kingdom); Donders Institute for Brain, Cognition and Behaviour (Netherlands); Universitat Pompeu Fabra (Spain); The University of Melbourne (Australia); Parc Sanitari Sant Joan de Déu (Spain); McGovern Institute for Brain Research (United States); Massachusetts Institute of Technology (United States)
Journal: Biological psychiatry global open science, volume 6, issue 5, article 100753
Dates: received 20 May 2025; accepted 16 April 2026; published online 12 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.bpsgos.2026.100753 · PMID 42518781 · PMCID PMC13382308 · OpenAlex W7161134056
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), depression (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging
Keywords: Depression, Dynamic brain connectivity, Mechanisms, Mindfulness, Rumination
Journal subjects: Archival Report
Topic: Mindfulness and Compassion Interventions (Clinical Psychology, Psychology), according to OpenAlex
Funding: Carlsberg Foundation Fellowships (CF21_0645); Brain and Behavior Research Foundation
Citations: not cited yet (Europe PMC); 58 references in the paper

Abstract

Background: Depression is a prevalent and debilitating affective disorder characterized by the dominance and persistence of depressive rumination. Mindfulness-based cognitive therapy (MBCT) is an effective treatment for recurrent depression developed specifically to target rumination and recurrence risk by training metacognitive awareness and adaptive attention, emotion, and self-regulation skills. However, the underlying mechanisms by which mindfulness training impacts maladaptive depressive rumination are not well understood, and a deeper understanding of its effects on the complex brain dynamics during depressive rumination is needed.

Methods: In a randomized controlled functional magnetic resonance imaging (fMRI) study (N = 80), we examined dynamic neural changes during resting-state fMRI of an experimentally induced rumination state before and after treatment with MBCT (n = 27) for recurrent depression in addition to treatment as usual (TAU) or TAU alone (n = 21). More specifically, we characterized the changes during a depressive rumination state as a repertoire of metastable substates, each with an occurrence frequency (fractional occupancy) and stability (lifetimes).

Results: We found that MBCT training compared with TAU altered the fractional occupancy of a salience-somatomotor metastable substate during the depressive rumination state. These dynamic network changes in turn were associated with reduced trait rumination posttreatment and reduced depressive symptoms at the 3-month follow-up.

Conclusions: In a ruminative state, changes in the dynamics of the somatosensory-salience network following mindfulness training was associated with improved clinical outcomes and reduced trait rumination, which may provide insight into candidate brain mechanisms or markers of treatment response to MBCT.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

PSYMARKER/leida-matlab

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: ab03cbb6f987a80fd65ed1aebe75419b660b907e, 29 January 2025
Languages: MATLAB (49)
Size: 55 files, 49 scripts
Software Heritage: not checked
Found in: the acknowledgements
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (13 files), Image Processing Toolbox (11 files), Signal Processing Toolbox (3 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
51 files
At the source: github.com/PSYMARKER/leida-matlab

Tracing map

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  • 49 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 5 keywords, 2 funders, 48 references.

Cite

This paper

van der Velden, A. M., Vohryzek, J., Dagnino, P. C., Kuyken, W., Montero-Marin, J., Collin, G., Kringelbach, M. L., & Ruhe, H. G. (2026). Dynamic Repertoire of Brain Networks in Mindfulness-Based Cognitive Therapy During Rumination: A Randomized Controlled Trial. Biological psychiatry global open science, 6(5), 100753. https://doi.org/10.1016/j.bpsgos.2026.100753

BibTeX

@article{vandervelden2026dynamic,
author = {van der Velden, Anne Maj and Vohryzek, Jakub and Dagnino, Paulina Clara and Kuyken, Willem and Montero-Marin, Jesus and Collin, Guusje and Kringelbach, Morten L. and Ruhe, Henricus G.},
title = {{Dynamic Repertoire of Brain Networks in Mindfulness-Based Cognitive Therapy During Rumination: A Randomized Controlled Trial}},
journal = {Biological psychiatry global open science},
year = {2026},
month = may,
volume = {6},
number = {5},
pages = {100753},
publisher = {Elsevier},
issn = {2667-1743},
doi = {10.1016/j.bpsgos.2026.100753},
url = {https://doi.org/10.1016/j.bpsgos.2026.100753},
pmid = {42518781},
pmcid = {PMC13382308}
}

RIS

TY - JOUR
AU - van der Velden, Anne Maj
AU - Vohryzek, Jakub
AU - Dagnino, Paulina Clara
AU - Kuyken, Willem
AU - Montero-Marin, Jesus
AU - Collin, Guusje
AU - Kringelbach, Morten L.
AU - Ruhe, Henricus G.
TI - Dynamic Repertoire of Brain Networks in Mindfulness-Based Cognitive Therapy During Rumination: A Randomized Controlled Trial
T2 - Biological psychiatry global open science
J2 - Biol Psychiatry Glob Open Sci
PY - 2026
DA - 2026/05/12
VL - 6
IS - 5
SP - 100753
SN - 2667-1743
PB - Elsevier
DO - 10.1016/j.bpsgos.2026.100753
UR - https://doi.org/10.1016/j.bpsgos.2026.100753
LA - en
ER -

CSL-JSON

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